System and method for separating cardiac signals

ABSTRACT

EKG sensors (( 150 ) are placed on a patient ( 140 ) to receive electrocardiogram (EKG) recording signals, which are typically combinations of original signals from different sources, such as pacemaker signals, QRS complex signals, and irregular oscillatory signals that suggest an arrhythmia condition. A computing module ( 120 ) uses independent component analysis to separate the recorded EKG signals. The separated signals are displayed to help physicians to analyze heart conditions and to identify probably locations of abnormal heart conditions. At least a portion of the separated signals can be further displayed in a chaos phase space portrait to help detect abnormality in heart conditions.

BACKGROUND OF THE INVENTION CROSS REFERENCE TO RELATED APPLICATION

The present application is the U.S. national stage application ofInternational Application PCT/US02/21277, filed Jul. 3, 2002, which waspublished on Jan. 16, 2003 as International Publication Number WO03/003905.

1. Field of the Invention

The present invention relates to medical devices for recording cardiacsignals and separating the recorded cardiac signals.

2. Description of the Related Art

Electrocardiogram (EKG) recording is a valuable tool for physicians tostudy patient heart conditions. In a typical 12-lead arrangement, up to12 sensors are placed on a subject's chest or abdomen and limbs torecord the electric signals from the beating heart. Each sensor, alongwith a reference electrode, form a separate channel that produces anindividual signal. The signals from the different sensors are recordedon an EKG machine as different channels. The sensors are usuallyunipolar or bipolar electrodes or other devices suitable for measuringthe electrical potential on the surface of a human body. Since differentparts of the heart, such as the atria and ventricles, produce differentspatial and temporal patterns of electrical activity on the bodysurface, the signals recorded on the EKG machine are useful foranalyzing how well individual parts of the heart are functioning.

A typical heartbeat signal has several well-characterized components.The first component is a small hump in the beginning of a heartbeatcalled the “P-Wave”. This signal is produced by the right and leftatria. There is a flat area after the P-Wave which is part of what iscalled the PR Interval. During the PR interval the electrical signal istraveling through the atrioventricular node (AV) node. The next largespike in the heartbeat signal is called the “QRS Complex.” The QRSComplex is tall, spikey signal produced by the ventricles. Following theQRS complex is another smaller bump in the signal called the “T-Wave,”which represents the electrical resetting of the ventricles inpreparation for the next signal. When the heart beats continuously, theP-QRS-T waves repeat over and over.

Many publications have described studying cardiac signals and detectingabnormal heart conditions. Sample publications include U.S. PatentPublication No. 20020052557; Podrid & Kowey, Cardiac Arrhythmia:Mechanisms, Diagnosis, and Management Lippincott Williams & WilkinsPublishers (2nd edition, Aug. 15, 2001); Marriott & Conover, AdvancedConcepts in Arrhythmias, Mosby Inc. (3nd edition, Jan. 15, 1998); andJosephson, M. E., Clinical Cardiac Electrophysiology: Techniques andInterpretations, Lippincott Williams & Wilkins Publishers; ISBN (3rdedition, Dec. 15, 2001).

Unfortunately, although EKG signals have been studied for decades, theyare difficult to assess because EKG signals recorded at the surface aremixtures of signals from multiple sources. Typically, it is relativelystraightforward to measure the shape of the QRS complex since thissignal is so strong. However, irregular shaped P-wave or T-wave signals,along with weak irregular oscillatory signals that suggest a heartarrhythmia are often masked by large pacemaker signals, or the strongQRS complex signals. Thus, it can be very difficult to isolate smallirregular oscillatory signals and to identify arrhythmia conditions.

In addition, atrial and ventricular signals are sometimes undesirablysuperimposed over one another. In many cases, diagnosis of diseasestates requires these signals to be separated from one another. Forexample, it might be desirable to separate P wave signals from QRScomplex signals, so that signals originating in an atrium are isolatedfrom signals representing concurrent activities in the ventricle.

In some practices the EKG signals are electronically “filtered” byexcluding signals of certain frequencies. The signals are also“averaged” to remove largely random or asynchoronous data, which isassumed to the meaningless “noise.” The filtering and averaging methodsirreversibly eliminate portions of the recorded signals. In addition, itis not proven whether the more random data is truly “noise” and trulymeaningless. It might be that the signals that are removed areindicative of a disease state in a patient. Another method as disclosedin U.S. Pat. No. 6,308,094 entitled “System for prediction of cardiacarrhythmias” uses Karhunen Loeve Transformation to decompose or compresscardiac signals into elements that are deemed “significant.” As a resultthe information that are deemed “insignificant” are lost.

Compared to other signal separation applications, separating EKGrecording signals presents additional challenges. For example, thesources are not always stationary since the heart chambers contract andexpand during beating. Additionally, the activity of a single chambermay be mistaken for multiple sources because of the presence of movingwaves of electrical activity across the heart. If electrodes are notsecurely attached to the patient, or if the patient moves (for exampleolder patients may suffer from uncontrolled jittering), the movement ofthe electrodes also undesirably generates signals. In addition, multiplesignals can be sensed by the EKG which are unrelated to the cardiacsignature, such as myopotentials, i.e., electrical signals from musclesother than the heart.

There has been disclosure of cardiac rhythm management systems thatstore of list of triggers. U.S. Pat. No. 6,400,982 entitled “Cardiacrhythm management system with arrhythmia prediction and prevention”discloses such a system. If a trigger matches detected cardiac signalsfrom a patient, the system calculates the probability of arrhythmia andactivates a prevention therapy to the patient. However the cardiacsignals are in fact mixtures of signals from multiple sources, and thesignals that are important for arrhythmia detection can be masked byother signals. It is therefore desirable to separate the cardiac signalsused in the cardiac rhythm management systems.

Independent component analysis (ICA) is a technique for separating mixedsource signals (components) which are presumably independent from eachother. In its simplified form, independent component analysis operates a“un-mixing” matrix of weights on the mixed signals, for examplemultiplying the matrix with the mixed signals, to produce separatedsignals. The weights are assigned initial values, and then adjusted tominimize information redundancy in the separated signals. Because thistechnique does not require information on the source of each signal, itis known as a “blind source separation” method. Blind separationproblems refer to the idea of separating mixed signals that come frommultiple independent sources. Although there are many ICA techniquescurrently known, most have evolved from the original work described inU.S. Pat. No. 5,706,402 issued on Jan. 6, 1998. Additional references ofICA and blind source separation can be found in, for example, A. J. Belland T J Sejnowski, Neural Computation 7:1129-1159 (1995)); Te-Won Lee,Independent Component Analysis: Theory and Applications, Kluwer AcademicPublishers, Boston, September 1998, Hyvarinen et al., IndependentComponent Analysis, 1st edition (Wiley-Interscience, May 18, 2001); MarkGirolami, Self-Organizing Neural Networks: Independent ComponentAnalysis and Blind Source Separation (Perspectives in Neural Computing)(Springer Verlag, September 1999); and Mark Girolami (Editor), Advancesin Independent Component Analysis (Perspectives in Neural Computing)(Springer Verlag August 2000). Single value decomposition algorithmshave been disclosed in Adaptive Filter Theory by Simon Haykin (ThirdEdition, Prentice-Hall (NJ), (1996).

There has been suggestion to use chaos theory to analyze cardiac signalsto detect abnormal heart conditions. Sample disclosures include U.S.Pat. Nos. 5,439,004, 5,342,401, 5,447,520 and 5,456,690; PCT applicationNos. WO02/34123 and WO0224276; Smith et al. Electrical Alternans andCardiac Electrical Instability. Circulation, Vol. 77, No. 1, pp. 110-121(January 1988). Other approaches are disclosed in U.S. Pat. No.5,447,520 issued to Spano, et al. and U.S. Pat. No. 5,201,321 issued toFulton. Chaos theory is defined as the study of complex nonlineardynamic systems. Complex implies just that, nonlinear implies recursionand higher mathematical algorithms, and dynamic implies non-constant andnon-periodic. Thus chaos theory is, very generally, the study ofchanging complex systems based on mathematical concepts of recursion,whether in the form of a recursive process or a set of differentialequations modeling a physical system.

When a bounded chaotic system has some kind of long-term pattern, butthe pattern is not a simple periodic oscillation or orbit, then thesystem has a “Strange Attractor”. If the system's behavior is plotted ina graph over an extended period patterns can be discovered that are notobvious in the short term. In addition, in these types of systems, nomatter what the initial conditions are, usually the same pattern isfound to emerge. The area for which this recurring pattern holds true iscalled the “basin of attraction” for the attractor. Chaos theory methodshave been described in, for example, N. H. Packard, J. P. Crutchfield,J. Doyne Farmer, and R. S. Shaw, Geometry of a Time Series, PhysicalReview Letters, 47 (1980), p. 712; F. Takens, Detecting StrangeAttractors in Turbulence in Lecture Notes in Mathematics 898, D. A. Randand L. S. Young, eds., (Berlin: Springer-Verlag, 1981), p. 336; and J.P. Crutchfield, J. Doyne Farmer, N. H. Packard, and R. S. Shaw, OnDetermining the Dimension of Chaotic Flows, Physica 3D, (1981), pp.605-17.

For all of these reasons, what is needed in the art is a system that canaccurately separate medical signals from one another in order todiagnose disease states.

SUMMARY OF THE INVENTION

The present application discloses systems and methods for usingindependent component analysis to determine the existence and locationof anomalies such as arrhythmias of a heart. The disclosed systems andmethods can be applied to suggest the location of atrial fibrillation,and to locate arrhythmogenic regions of a chamber of the heart usingheart cycle signals measured from a body surface of the patient.Non-invasive localization of the ectopic origin allows focal treatmentto be quickly targeted to effectively inhibit these complex arrhythmiaswithout having to rely on widespread and time consuming sequentialsearches or on massively invasive simultaneous intracardiac sensortechnique. The effective localization of these complex arrhythmias canbe significantly enhanced by using independent component analysis toseparate superimposed heart cycle signals originating from differingchambers or regions of the heart tissue. In addition, the signals thatare separated by ICA are preferably also analyzed by plotting them on achaos phase space portrait.

One aspect of the invention relates to a medical system for separatingcardiac signals. This aspect includes a receiving module to receiverecorded cardiac signals from medical sensors, a computing module toseparate the received signals using independent component analysis toproduce separated signals, and a display module to display the separatedsignals.

Another aspect of the invention relates to a method of detectingarrhythmia in a patient. The method includes placing EKG sensors on apatient to produce recorded EKG signals, sending the recorded signals toa computing module to separate the recorded signals into separatedsignals using independent component analysis, and reviewing a display ofthe separated signals to determine the existence of arrhythmia in thepatient. In a preferred embodiment, each component of separated signalscorresponds to a channel of recorded signals and its sensor location,therefore when the one or more components of separated signals thatsuggest arrhythmia are detected, the corresponding one or more sensorlocations also suggest the location of arrhythmia.

Yet another aspect of the invention relates to a cardiac rhythmmanagement system. The system includes a cardiac signal recording moduleto record cardiac signals of a patient, a computing module to separatethe recorded signals into separated signals using independent componentanalysis, and a detection module to detect or to predict an abnormalcondition based on analyzing the separated signals. The system alsoincludes a treatment module to treat the patient or a warning module toissue a warning when the abnormal condition is detected or predicted.

Other aspects and embodiments of the invention are described below inthe detailed description section or defined by the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of a EKG system according to one embodiment of theinvention.

FIG. 2 is a flowchart illustrating one embodiment of a process forseparating cardiac signals.

FIG. 3A is a sample chart of recorded EKG signals.

FIG. 3B is a sample chart of separated EKG signals.

FIG. 3C is a sample chart of one component of separated signals backprojected on the recorded signals.

FIG. 4A is a chaos phase space portrait of three components of separatedEKG signals of a healthy subject.

FIG. 4B is a chaos phase space portrait of three components of separatedEKG signals of a subject with an abnormal heart condition.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Embodiments of the invention relate to a system and method foraccurately separating medical signals in order to determine diseasestates in a patient. In one embodiment, the system analyzes EKG signalsin order to determine whether a patient has a heart ailment orirregularity. As discussed in detail below, embodiments of the systemutilize the techniques of independent component analysis to separate themedical signals from one another.

In addition to the signal separation technique, embodiments of theinvention also relate to systems and methods that first separate signalsusing ICA, and then perform an analysis on a specific isolated signal,or set of isolated signals, using a “chaos” analysis. As describedearlier, Chaos theory (also called nonlinear dynamics) studies patternsthat are not completely random, but cannot be determined by simpleformulas. Because cardiac signals are typically non-random, but cannotbe easily described by a simple formula, Chaos theory analysis asdescribed below provides an effective tool to analyze these signals anddetermine disease states.

Accordingly, once the signals are separated using ICA, they can beplotted to produce a chaos phase space portrait. By reviewing thepatterns in the phase space portrait, for example reviewing theexistence and location of one or more attractors, or comparingestablished health patterns and established abnormal patterns with thepatterns of the patient, a user is able to assess the likelihood ofabnormality in the signals, which indicate disease conditions in thepatient.

FIG. 1 is a diagram of an EKG system that includes a computing modulefor signal separation according to one embodiment of the presentinvention. As shown in FIG. 1, electrode sensors 150 are placed on thechest and limb of a patient 140 to record electric signals. Theelectrodes send the recorded signals to a receiving module 110 of theEKG system 100. After optionally performing signal amplification,analog-to-digital conversion or both, the receiving module 110 sends thereceived signals to a computing module 120 of the EKG system 100. Thecomputing module 120 uses an independent component analysis method toseparate the recorded signals to produce separated signals. Theindependent component analysis method has been described in detail inthe Appendix and below with respect to FIG. 2.

The computing module 120 can be implemented in hardware, software, or acombination of both. It can be located physically within the EKG system100 or connected to the recorded signals received by the EKG system 100.A displaying module 130, which includes a printer or a monitor, displaysthe separated signals on paper or on screen. The displaying module 130can be located within the EKG system 100 or connected to it. Optionally,the displaying module 130 also displays the recorded signals on paper oron screen. In one embodiment, the displaying module also displays somecomponents of the separated signals in a chaos phase space portrait.

In one embodiment, the EKG system 100 also includes a database (notshown) that stores recognized EKG signal triggers and correspondingdiagnosis. The triggers refer to conditions that indicate the likelihoodof arrhythmia. For example, triggers can include sinus beats, prematuresinus beats, beats following long sinus pauses, long-short beatsequences, R on T-wave beats, ectopic ventricular beats, prematureventricular beats, and so forth. Triggers can include threshold valuesthat indicate arrhythmia, such as threshold values of ST elevations,heart rate, increase or decrease in heart rate, late-potentials,abnormal autonomic activity, and so forth. A left bundle-branch blockdiagnosis can be associated with triggers such as the absence of q wavein leads I and V6, a QRS duration of more than 120 msec, small notchingof R wave, etc.

Triggers can be based on a patient's history, for example the percentageof abnormal beats detected during an observation period, the percentageof premature or ectopic beats detected during an observation period,heart rate variation during an observation period, and so forth.Triggers may also include, for example, the increase or decrease of STelevation in beat rate, the increase in frequency of abnormal orpremature beats, and so forth.

A matching module (not shown) attempts to match the separated signalswith one or more of the stored triggers. If a match is found, thematching module displays the matched corresponding diagnosis, or sends awarning to a healthcare worker or to the patient. Methods such ascomputer-implemented logic rules, classification trees, expert systemrules, statistical or probability analysis, pattern recognition,database queries, artificial intelligence programs and others can beused to match the separated signals with stored triggers.

FIG. 2 is a flowchart illustrating one embodiment of a process forseparating EKG signals. The process starts from a start block 202, andproceeds to a block 204, where the computing module 120 of the EKGsystem 100 receives the recorded signals X_(j) from the electrodesensors, with J being the number of channels. Prior to processing, thesignals can be amplified to strengths suitable for computer processing.Analog-to-digital conversion of signals can also be performed.

From the block 204, the process proceeds to a block 206, where theinitial values for a “un-mixing” matrix of scaling weights W_(ij) areselected. In one embodiment, the initial values for a matrix of initialweights W_(i0) are also selected. The process then proceeds to a block208, where a plurality of training signals Y_(i) are produced byoperating the matrix on the recorded signals. In a preferred embodiment,the training signals are produced by multiplying the matrix with therecorded signals such that Y_(i)=W_(ij)*X_(j). In one embodiment, theinitial weights W_(i0) are included such that Y_(i)=W_(ij)*X_(j)+W_(i0).The process proceeds from the block 208 to a block 210, wherein thescaling weights W_(ij) and optionally the initial weights W_(i0) areadjusted to reduce the information redundancy among the trainingsignals. Methods of adjusting the weights have been described in theAppendix.

The process proceeds to a decision block 212, where the processdetermines whether the information redundancy has been reduced to asatisfactory level. The criteria for the determination has beendescribed in the Appendix. If the process determines that informationredundancy among the training signals has been reduced to a satisfactorylevel, then the process proceeds to a block 214, where the trainingsignals are displayed as separated signals Y_(i), with I being thenumber of components for the separated signals. In a preferredembodiment, I, the number of components of separated signals, is equalto J, the number of channels of recorded signals. Otherwise the processreturns from the block 212 to the block 208 to again adjust the weights.From the block 214, the process proceeds to an end block 216.

For the un-mixing matrix W with the final weight values, its rowsrepresent the time courses of relative strengths/activity levels (andrelative polarities) of the respective separated components. Its weightsgive the surface topography of each component, and provide evidence forthe components' physiological origins. For the inverse of matrix W, itscolumns represent the relative projection strengths (and relativepolarities) of the respective separated components onto the channels ofrecorded signals. The back projection of the ith independent componentonto the recorded signal channels is given by the outer product of theith row of the separated signals matrix with the ith column of theinverse un-mixing matrix, and is in the original recorded signals. Thuscardiac dynamics or activities of interest accounted for by single or bymultiple components can be obtained by projecting one or more ICAcomponents back onto the recorded signals, X=W⁻¹*Y, where Y is thematrix of separated signals, Y=W*X.

The separated signals are determined by the ICA method to bestatistically independent and are presumed to be from independentsources. Regardless of whether there is in fact some dependence betweenthe separated EKG signals, test results show that the separated signalsprovide a beneficial perspective for physicians to detect and to locatethe abnormal heart conditions of a patient.

In a preferred embodiment, time-delay between source signals is ignored.Since the sampling frequencies of cardiac signals are in the relativelylow 200-500 Hz range, the effect of time-delay can be neglected.

Improved methods of ICA can be used to speed up the signal separationprocess. In one embodiment, a generalized Gaussian mixture model is usedto classify the recorded signals into mutually exclusive classes. Theclassification methods have been disclosed in U.S. patent applicationSer. No. 09/418,099 titled “Unsupervised adaptation and classificationof multiple classes and sources in blind source separation” and PCTApplication No. W00127874 titled “Unsupervised adaptation andclassification of multi-source data using a generalized Gaussian mixturemodel.” In another embodiment, the computing module 120 incorporates apriori knowledge of cardiac dynamics, for example supposing separatedQRS components to be highly kurtotic and (ar)rythmic component(s) to besub-Gaussian. ICA methods with incorporated a priori knowledge have beendisclosed in T-W. Lee, M. Girolami and T. J. Sejnowski, IndependentComponent Analysis using an Extended Infomax Algorithm for MixedSub-Gaussian and Super-Gaussian Sources, Neural Computation, 1999,Vol.11(2): 417-441.

FIG. 3A illustrates a ten-second portion of 12 channels of signals thatwere gathered as part of an EKG recording. The horizontal axis in FIG.3A represents time progression of ten seconds. The vertical axisrepresents channel numbers 1 to 12. The signals of FIG. 3A are, in thiscase, from a patient that provided a mixture of multiple signals,including QRS complex signals, pacemaker signals, multiple oscillatoryactivity signals, and noise. However, because these signals were alloccurring simultaneously, they cannot be easily separated from oneanother using conventional EKG equipment.

In contrast, FIG. 3B illustrates output signals separated from themixture signals of FIG. 3A, according to one embodiment of the presentinvention. As above, the horizontal axis in FIG. 3B represents timeprogression of ten seconds and the vertical axis represents theseparated components 1 to 12. The separated signals in FIG. 3B aredisplayed as components 1 to 12 corresponding to the channels 1 to 12 inFIG. 3A, so that a physician can identify a separated signal as relatingto its respective recorded signal's corresponding sensor location on thepatient body. For example, in a standard 12-lead arrangement, leads II,III and AvF represent signals from the inferior region. Leads V1, V2represent signals from the septal region. Leads V5, V6, I, and a VLrepresent signals from the lateral heart. Right and posterior heartregions typically require special lead placement for recording. Tobetter identify the location of a heart condition, more than 12 leadscan be used. For example, 20, 30, 40, 50, or even hundreds of sensorscan be placed on various portions of a patient's torso. Fewer than 12leads can also be used. The sensors are preferably non-invasive sensorslocated on the patient's body surface, but invasive sensors can also beused. With separated signals each corresponding to one of the locations,a physician can review the signals and detect abnormalities thatcorrespond to the respective locations.

As shown in FIG. 3B, the component #1 represents the pacemaker signalsand the early part of QRS complex signals. The component #2 representsmajor portions of later parts of the QRS complex signals. QRS complexsignals represent the depolarization of the left ventricle. Thecomponent #10 represents atrial fibrillation (a type of arrhythmia)signals. Therefore atrial fibrillation is predicted to be located at thesensor location that corresponds to channel #10. Although components #1and #10 contain similar frequency contents of oscillatory activitybetween heart beats, they capture activities from different spatiallocations.

For EKG signals, we discovered that the signals separated using ICA areusually more independent from each other and have less informationredundancy than signals that have not been processed through ICA.Compared to the recorded signals, the separated signals usually betterrepresent the signals from the original sources of the patient's heart.In addition to arrhythmia, the separated cardiac signals can also beused to help detect other heart conditions. For example, the separatedsignals especially the separated QRS complex signals can be used detectpremature ventricular contraction. The separated signals especially theseparated Q wave signals can be used to detect myocardial infarction.Separating the EKG signals, especially separating the QRS complex and Twave signals, can help distinguish left and right bundle branch block.

Of course, the disclosed system and method are not limited to detectingarrhythmia, or any particular type of disease state. Embodiments of theinvention include all methods of analyzing medical signals using ICA.For example, when a pregnant woman undergoes EKG recording, the heartsignals from the woman and from the fetus(es) can be separated.

The separated cardiac signals can be characterized as non-random but noteasily deterministic, which make them suitable subjects for chaoticanalysis. As mentioned above, chaos theory (also called nonlineardynamics) studies patterns that are not completely random but cannot bedetermined by simple formulas. The separated signals can be plotted toproduce a chaos phase space portrait. By reviewing the patterns in thephase space portrait, including the existence and location of one ormore attractors, a user is able to assess the likelihood of abnormalityin the signals, which indicate disease conditions in the patient.

In a preferred embodiment, the QRS complex signals are separated intothree different components, with each component representing a portionof the QRS complex. The 3 components are 3 data sets that are found tobe temporally statistically independent using independent componentanalysis. Using the three components, a 3-dimensional phase spaceportrait of QRS complex can be displayed to show the trajectory of thethree components.

FIG. 3C is a sample chart of the component #10 of separated signals (asshown in FIG. 3B) back projected onto the recorded signals of FIG. 3A.The separated signals of component #10, which indicate arrhythmia, isidentified by reference number 302 in FIG. 3C. The 12 channels ofrecorded signals are identified by reference number 304 for ease ofidentification. FIG. 3C therefore allows direct visual comparison of aseparated component against channels of recorded signals. The backprojections of cardiac dynamics allow us to exam the amount ofinformation accounted for by single or by multiple components in therecorded signals and to confirm the components' physiological meaningssuggested by the surface topography (the aforementioned inverse ofcolumns of the un-mixing matrix).

FIG. 4A illustrates the phase space portrait of the EKG recording of ahealthy subject. FIG. 4B illustrates the phase space portrait of the EKGrecording of an atrial fibrillation patient. In FIGS. 4A and 4B, the x,y, and z axis represent the amplitudes of the 3 QRS components. Theseparated signals' values over time are plotted to produce the phasespace portraits. In the healthy EKG recording of FIG. 4A, the densecluster 402 indicates the existence of an attractor that attracts thesignal values to the region of the dense cluster 402. The dense cluster402 represents the most frequent occurrences of the signals. In theatrial fibrillation patient EKG recording of FIG. 4B, an additional loop404, which is not part of the dense cluster 402, is below the attractorand the dense cluster 402 and closer to the base plane than the densecluster 402. This additional loop 404 is presumably due to theoscillatory activity in the baseline portions of the EKG signals. Theseparated component #10 signal that indicate an arrhythmia condition ispresumably responsible for the additional loop 404. The visual patterncan be compared with the visual pattern of a health subject and manuallyrecognized as probative of indicating an abnormal condition such asatrial fibrillation.

Instead of the 3 QRS complex components as shown in FIG. 413, othercomponents or more than 3 components can also be used to plot the chaosphase space portrait. If more than 3 components are used, the differentcomponents can be plotted in different colors. The 3 QRS complexcomponents of FIG. 4B are selected because test results suggest thatsuch a phase space portrait is physiological significant and functionsusually well as an indication of a patient's heart condition.

Although FIGS. 3A, 3B, 4A and 4B were produced using test resultsrelated to the detection and localization of focal atrial fibrillation,the disclosed systems and methods can be used to detect and to localizeother heart conditions including focal and re-entrant arrhythmia. Thedisclosed systems and methods can also be used to detect and to localizeparoxysmal atrial fibrillation as well as persistent and chronic atrialfibrillation.

The disclosed methods can be used to improve existingcardioverter/defibrillators (ICD's) that can deliver electrical stimulito the heart. In addition to existing ICD's and existing pacemakers,some of the existing cardiac rhythm management devices also combine thefunctions of pacemakers and ICD's. A computing module embodying thedisclosed methods can be added to the existing systems to separate therecorded cardiac signals. The separated signals are then used by thecardiac rhythm management systems to detect or to predict abnormalconditions. Upon detection or prediction, the cardiac rhythm managementsystem automatically treats the patient, for example by deliveringpharmacologic agents, pacing the heart in a particular mode, deliveringcardioversion/defibrillation shocks to the heart, or neural stimulationof the sympathetic or parasympathetic branches of the autonomic nervoussystem. Instead of or in addition to automatic treatment, the system canalso issue a warning to a physician, a nurse or the patient. The warningcan be issued in the form of an audio signal, a radio signal, and soforth. The disclosed signal separation methods can be used in cardiacrhythm management systems in hospitals, in patient's homes or nursinghomes, or in ambulances. The cardiac rhythm management systems includeimplantable cardioverter defibrillators, pacemakers, biventricular orother multi-site coordination devices and other systems for diagnosticEKG processing and analysis. The cardiac rhythm management systems alsoinclude automatic external defibrillators and other external monitors,programmers and recorders.

In one embodiment, an improved cardiac rhythm management system includesa storage module that stores the separated signals. In one arrangement,the storage module can be removed from the cardiac rhythm managementsystem and connected to a computing device. In another arrangement, thestorage module is directly connected to a computing device without beingremoved from the cardiac rhythm management system. The computing devicecan provide further analysis of the separated signals, for exampledisplaying a chaos phase space portrait using some of the separatedsignals. The computing device can also store the separated signals toprovide a history of the patient's cardiac signals.

The disclosed methods can also be applied to predict the occurrence ofarrhythmia within a patient's heart. After separating recorded EKGsignals into separated signals, the separated signals can be matchedwith stored triggers and diagnosis as described above. If the separatedsignals match stored triggers that are associated with arrhythmia, anoccurrence of arrhythmia is predicted. In other embodiments, anarrhythmia probability is then calculated, for example based on howclosely the separated signals match the stored triggers, based onrecords of how frequently in the past has the patient's separatedsignals matched the stored triggers, and/or based on how frequently inthe past the patient has actually suffered arrhythmia. The calculatedprobability can then be used to predict when will the next arrhythmiaoccur for the patient. Based on statistics and clinical data, calculatedprobabilities can be associated with specified time periods within anarrhythmia will occur.

In addition to EKG signals, the disclosed systems and methods can beapplied to separate other electrical signals such aselectroencephalogram signals, electromyographic signals,electrodermographic signals, and electroneurographic signals. They canbe applied to separate other types of signals, such as sonic signals,optic signals, pressure signals, magnetic signals and chemical signals.The disclosed systems and methods can be applied to separate signalsfrom internal sources, for example within a cardiac chamber, within ablood vessel, and so forth. The disclosed systems and methods can beapplied to separate signals from external sources such as the skinsurface or away from the body. They can also be applied to record and toseparate signals from animal subjects.

Although the foregoing has described certain preferred embodiments,other embodiments will be apparent to those of ordinary skill in the artfrom the disclosure herein. Additionally, other combinations, omissions,substitutions and modifications will be apparent to the skilled artisanin view of the disclosure herein. Accordingly, the present invention isnot to be limited by the preferred embodiments, but is to be defined byreference to the following claims.

The present application incorporates by reference U.S. Pat. No.5,706,402, titled “Blind signal processing system employing informationmaximization to recover unknown signals through unsupervisedminimization of output redundancy” filed Nov. 28, 1994 in its entiretyas an APPENDIX as follows.

1. A medical system for processing electrocardiogram (EKG) signals, themedical system comprising: a receiving module configured to receive aplurality J of recorded EKG signals X_(j) from a plurality of EKGsensors, one of the recorded EKG signals per one of the EKG sensors; acomputing module configured to separate the received signals usingindependent component analysis to produce a plurality I of statisticallyindependent separated signals Y_(i); a display module configured todisplay the separated signals; a database storing a plurality of EKGsignal triggers and one or more corresponding diagnoses, wherein atleast one of the EKG signal triggers is based on history of the patientduring an observation period; and a matching module configured to matchthe separated signals with one or more of the stored EKG signaltriggers, the one or more of the stored EKG signal triggers comprisingthe at least one of the EKG signal triggers based on history of thepatient during the observation period.
 2. The medical system of claim 1,wherein the display module is further configured to display at least aportion of the separated signals in a chaos phase space portrait.
 3. Themedical system of claim 1, wherein the separated signals include threecomponents of QRS complex, and wherein the display module is furtherconfigured to display at least the three QRS complex components in achaos phase space portrait.
 4. The medical system of claim 1, whereinthe computing module is configured to separate the recorded signals byperforming steps comprising multiplying a vector of the recorded signalsby a matrix W_(ij) such that Y_(i)=W_(ij)*X_(j).
 5. The medical systemof claim 1, wherein the computing module is configured to separate therecorded signals using a neural-network implemented method, theneural-network implemented method comprising: selecting a plurality I ofbias weights W_(i0) and a plurality I*J of scaling weights W_(ij);adjusting the bias weights W_(i0) and the scaling weights W_(ij) tominimize information redundancy among separated signals; and producingthe statistically independent separated signals Y_(i) such thatY_(i)=W_(ij)*X_(j)+W_(i0).
 6. The medical system of claim 1, wherein,when the matching module matches one of the EKG signal triggers to aparticular diagnosis, the computing module causes the particulardiagnosis to be displayed.
 7. A computer-implemented method ofprocessing electrocardiogram (EKG) recording signals, the methodcomprising: receiving a first plurality of EKG recording signals fromEKG sensors placed on a patient, one of the EKG recording signals perone of the EKG sensors; separating the first plurality of EKG recordingsignals using independent component analysis to produce a secondplurality of statistically independent separated signals; storing aplurality of EKG signal triggers and one or more diagnoses, eachdiagnosis of the one or more diagnoses corresponding to at least one EKGsignal trigger of the plurality of EKG signal triggers, wherein the atleast one of the EKG signal triggers is based on history of the patientduring an observation period; matching the separated signals with one ormore of the stored EKG signal triggers, the one or more of the storedEKG triggers comprising the at least one EKG signal trigger; anddisplaying the separated signals.
 8. The method of claim 7, wherein thestep of displaying the separated signals comprises displaying at least aportion of the separated signals in a chaos phase space portrait.
 9. Themethod of claim 7, wherein the patient is a pregnant patient, andwherein the separated signals include separated signals originating fromthe pregnant patient and separated signals originating from a fetus. 10.The method of claim 7, further comprising calculating probability ofarrhythmia in the patient based on separation of the separated signalsfrom a match of one or more stored arrhythmia triggers.
 11. The methodof claim 7, wherein the displayed separated signals are used by aphysician to determine the likelihood of myocardial infarction in thepatient.
 12. The method of claim 7, wherein each of the separatedsignals corresponds to a location on the patient body, wherein thedisplayed separated signals are used by a physician to determine thelocation of an abnormal heart condition in the patient according to theseparated signals' corresponding locations.
 13. A cardiac rhythmmanagement system comprising: a cardiac signal recording moduleconfigured to record cardiac signals of a patient; a computing moduleconfigured to separate the recorded cardiac signals into statisticallyindependent separated signals using independent component analysis; adetection module configured to detect or predict an abnormal conditionbased on analyzing the separated cardiac signals; and a treatment moduleconfigured to treat the patient when the abnormal condition is detectedor predicted; wherein the detection module is configured to detect theabnormal condition by comparing the separated signals with a storedtrigger to determine whether the separated signals match the storedtrigger; the treatment module is configured to deliver one or moreelectrical stimuli to heart of the patient in response to detection orprediction of the abnormal condition by the detection module; and thestored trigger is based on history of the patient.
 14. A cardiac rhythmmanagement system comprising: a cardiac signal recording moduleconfigured to record cardiac signals of a patient; a computing moduleconfigured to separate the recorded cardiac signals into statisticallyindependent separated signals using independent component analysis; adetection module configured to detect or predict an abnormal conditionbased on analyzing the separated cardiac signals; and a warning moduleconfigured to issue a warning when the abnormal condition is detected orpredicted; wherein: the detection module is configured to detect orpredict the abnormal condition by comparing the separated signals withat least one stored trigger to determine whether the separated signalsmatch the at least one stored trigger; the treatment module isconfigured to deliver one or more electrical stimuli to heart of thepatient in response to detection or prediction of the abnormal conditionby the detection module; and the stored trigger is based on history ofthe patient.
 15. The cardiac rhythm management system of claim 14,wherein the at least one stored trigger comprises a plurality of storedtriggers.